Meeting energy (or power) demands of customers in a cost-effective way is an age-old problem in the energy utility industry. Traditionally, power has been supplied by bulk generation, where centrally-located power plants provide power through a transmission system to a distribution grid that provides power to end customers. As customers' power demands have evolved and existing systems have aged, additional pressures have been placed on such a traditional bulk generation scheme. For example, a bulk generation power system may be required to meet some quality of service (e.g., power quality, continuity of power, and/or voltage stability), which in turn may require upgrades to be made to a large portion of the bulk generation power system's infrastructure. Such upgrades can be quite expensive.
A modern approach to meeting power demand is distributed generation, where smaller non-centralized energy sources provide power to the distribution grid. With the advancement in various technologies, distributed generation has become a viable and cost effective option to deal with issues like quality of service, which avoids the difficulties in upgrading the infrastructure required by bulk generation. A different set of needs are placed on a distributed generation approach, such as generation scheduling. Since multiple non-centralized energy sources provide energy (or power) to the distribution grid, such energy sources need to be optimally scheduled to produce enough power to meet customer demand in a cost-effective manner. For example, the costs involved in power generation at a first energy source may be more expensive than at a second energy source, and thus the first energy source may be scheduled to generate a limited amount of power in order to save costs.
Optimal scheduling of energy sources is challenging, especially considering that a standard for scheduling, managing, and operating energy sources does not presently exist. Conventional scheduling optimization techniques include “Dynamic Programming” and “Lagrange Relaxation.” However, such conventional techniques are computationally demanding, as well as difficult to implement and test. Further, non-centralized energy sources can be quite varied and non-standard in their capabilities and restrictions, which makes the application of such conventional techniques even more difficult.